"""
@author:  liaoxingyu
@contact: sherlockliao01@gmail.com
"""

import math

import torch
from torch import nn




def conv3x3(in_planes,out_planes,stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes,out_planes,kernel_size=3,stride=stride,
                     padding=1,bias=False)

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self,inplanes,planes,stride=1,downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes,kernel_size=1,bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes,planes,kernel_size=3,stride=stride,
                               padding=1,bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes,planes*4,kernel_size=1,bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self,x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

class ResNet(nn.Module):

    def __init__(self,last_stride=1,block=Bottleneck,layers = [3,4,6,3]):
        self.inplanes = 64
        super().__init__()
        self.conv1 = nn.Conv2d(3,64,kernel_size=7,stride=2,padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
        self.layer1 = self._make_layer(block,64,layers[0])
        self.layer2 = self._make_layer(block,128,layers[1],stride=2)
        self.layer3 = self._make_layer(block,256,layers[2],stride=2)
        self.layer4 = self._make_layer(block,512,layers[3],stride=last_stride)


    def _make_layer(self,block,planes,blocks,stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes,planes * block.expansion,
                          kernel_size=1,stride=stride,bias=False),
                nn.BatchNorm2d(planes*block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes,planes,stride,downsample))
        self.inplanes = planes * block.expansion
        for i in range(1,blocks):
            layers.append(block(self.inplanes,planes))

        return nn.Sequential(*layers)

    def forward(self,x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)


        return x
